ReGO: Reference-Guided Outpainting for Scenery Image.

We present ReGO (Reference-Guided Outpainting), a new method for the task of sketch-guided image outpainting. Despite the significant progress made in producing semantically coherent content, existing outpainting methods often fail to deliver visually appealing results due to blurry textures and generative artifacts. To address these issues, ReGO leverages neighboring reference images to synthesize texture-rich results by transferring pixels from them.

Exploration of radionuclide labeling of a novel scFv-Fc fusion protein targeting CLDN18.2 for tumor diagnosis and treatment.

Purpose: Claudin 18.2 (CLDN18.2), due to its highly selective expression in tumor cells, has made breakthrough progress in clinical research and is expected to be integrated into routine tumor diagnosis and treatment.

CMID: Crossmodal Image Denoising via Pixel-Wise Deep Reinforcement Learning.

Removing noise from acquired images is a crucial step in various image processing and computer vision tasks. However, the existing methods primarily focus on removing specific noise and ignore the ability to work across modalities, resulting in limited generalization performance. Inspired by the iterative procedure of image processing used by professionals, we propose a pixel-wise crossmodal image-denoising method based on deep reinforcement learning to effectively handle noise across modalities.

[Lu]Lu-labeled anti-claudin-18.2 antibody demonstrated radioimmunotherapy potential in gastric cancer mouse xenograft models.

Purpose: Gastric cancer (GC), one of the most prevalent and deadliest tumors worldwide, is often diagnosed at an advanced stage with limited treatment options and poor prognosis. The development of a CLDN18.2-targeted radioimmunotherapy probe is a potential treatment option for GC.

Forecasting Treatment Outcomes Over Time Using Alternating Deep Sequential Models.

Medical decision making often relies on accurately forecasting future patient trajectories. Conventional approaches for patient progression modeling often do not explicitly model treatments when predicting patient trajectories and outcomes. In this paper, we propose Alternating Transformer (AL-Transformer) to jointly model treatment and clinical outcomes over time as alternating sequential models.

4D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancement.

Image enhancement aims at improving the aesthetic visual quality of photos by retouching the color and tone, and is an essential technology for professional digital photography. Recent years deep learning-based image enhancement algorithms have achieved promising performance and attracted increasing popularity. However, typical efforts attempt to construct a uniform enhancer for all pixels' color transformation.

TTVFI: Learning Trajectory-Aware Transformer for Video Frame Interpolation.

Video frame interpolation (VFI) aims to synthesize an intermediate frame between two consecutive frames. State-of-the-art approaches usually adopt a two-step solution, which includes 1) generating locally-warped pixels by calculating the optical flow based on pre-defined motion patterns (e.g.

Development of a CLDN18.2-targeting immuno-PET probe for non-invasive imaging in gastrointestinal tumors.

Claudin18.2 (CLDN18.2) is a tight junction protein that is overexpressed in a variety of solid tumors such as gastrointestinal cancer and oesophageal cancer.

Phase I study of MSB2311, a novel pH-dependent anti-PD-L1 monoclonal antibody, treating patients with advanced solid tumors and lymphoma.

MSB2311 is a novel pH-dependent humanized anti-programmed death-ligand 1 (PD-L1) monoclonal antibody. This phase I study primarily aimed to determine the maximum tolerated dose (MTD)/recommended phase 2 dose level (RP2D) of MSB2311 in patients with advanced solid tumors or lymphoma. MSB2311 was intravenously administered at 3, 10, and 20 mg/kg every 3 weeks (Q3W) and 10 mg/kg every 2 weeks (Q2W) using 3 + 3 design.

First-in-human CLDN18.2 functional diagnostic pet imaging of digestive system neoplasms enables whole-body target mapping and lesion detection.

Purpose: Claudin 18.2 (CLDN18.2) is a reliable target for lesion detection and could have clinical implications for epithelial tumors, especially digestive system neoplasms.

IL-1β Is an Androgen-Responsive Target in Macrophages for Immunotherapy of Prostate Cancer.

Great attention is paid to the role of androgen receptor (AR) as a central transcriptional factor in driving the growth of prostate cancer (PCa) epithelial cells. However, the understanding of the role of androgen in PCa-infiltrated immune cells and the impact of androgen deprivation therapy (ADT), the first-line treatment for advanced PCa, on the PCa immune microenvironment remains limited. On the other hand, immune checkpoint blockade has revolutionized the treatment of certain cancer types, but fails to achieve any benefit in advanced PCa, due to an immune suppressive environment.

Diversity-Learning Block: Conquer Feature Homogenization of Multibranch.

Visual Geometry Group (VGG)-style ConvNet is an neural-network process units (NPU)-friendly network; however, the accuracy of this architecture cannot keep up with other well-designed network structures. Although some reparameterization methods are proposed to remedy this weakness, their performance suffers from the homogenization issue of parallel branches, and the preset shape of convolution kernels also influences spatial perception. To address this problem, we propose a diversity-learning (DL) block to build the DLNet, which could adaptively learn various features to enrich the feature space.

Product Recognition for Unmanned Vending Machines.

Recently, the emerging concept of "unmanned retail" has drawn more and more attention, and the unmanned retail based on the intelligent unmanned vending machines (UVMs) scene has great market demand. However, existing product recognition methods for intelligent UVMs cannot adapt to large-scale categories and have insufficient accuracy. In this article, we propose a method for large-scale categories product recognition based on intelligent UVMs.

Gremlin1 is a therapeutically targetable FGFR1 ligand that regulates lineage plasticity and castration resistance in prostate cancer.

Among the greatest hurdles in clinical management of prostate cancer (PCa) are the progression to lethal castration-resistant prostate cancer (CRPC) and the lack of suitable targeted therapies for advanced disease. Here we identify Gremlin1 as a ligand for fibroblast growth factor receptor 1 (FGFR1), which promotes lineage plasticity and drives castration resistance. Importantly, we generate a specific anti-Gremlin1 therapeutic antibody and demonstrate synergistic effect with androgen deprivation therapy (ADT) in CRPC.

PicassoNet: Searching Adaptive Architecture for Efficient Facial Landmark Localization.

Since recent facial landmark localization methods achieve satisfying accuracy, few of them enable fast inference speed, which, however, is critical in many real-world facial applications. Existing methods typically employ complicated network structure and predict all the key points through uniform computation, which is inefficient since individual facial part might take different computation to obtain the best performance. Taking both accuracy and efficiency into consideration, we propose the PicassoNet, a lightweight cascaded facial landmark detector with adaptive computation for individual facial part.

Domain Adaptive Box-Supervised Instance Segmentation Network for Mitosis Detection.

The number of mitotic cells present in histopathological slides is an important predictor of tumor proliferation in the diagnosis of breast cancer. However, the current approaches can hardly perform precise pixel-level prediction for mitosis datasets with only weak labels (i.e.

A contrastive learning approach for ICU false arrhythmia alarm reduction.

The high rate of false arrhythmia alarms in Intensive Care Units (ICUs) can lead to disruption of care, negatively impacting patients' health through noise disturbances, and slow staff response time due to alarm fatigue. Prior false-alarm reduction approaches are often rule-based and require hand-crafted features from physiological waveforms as inputs to machine learning classifiers. Despite considerable prior efforts to address the problem, false alarms are a continuing problem in the ICUs.

Oriented Localization of Surgical Tools by Location Encoding.

Surgical tool localization is the foundation to a series of advanced surgical functions e.g. image guided surgical navigation.

Sketch-Guided Scenery Image Outpainting.

The outpainting results produced by existing approaches are often too random to meet users' requirements. In this work, we take the image outpainting one step forward by allowing users to harvest personal custom outpainting results using sketches as the guidance. To this end, we propose an encoder-decoder based network to conduct sketch-guided outpainting, where two alignment modules are adopted to impose the generated content to be realistic and consistent with the provided sketches.

Crowd Scene Analysis Encounters High Density and Scale Variation.

Crowd scene analysis receives growing attention due to its wide applications. Grasping the accurate crowd location is important for identifying high-risk regions. In this article, we propose a Compressed Sensing based Output Encoding (CSOE) scheme, which casts detecting pixel coordinates of small objects into a task of signal regression in encoding signal space.

Friction and Wear Characteristics of Microporous Interface Filled with Mixed Lubricants of M50 Steel at Different Loads.

Improving the tribological performance of M50 steel under the conditions of wide load range is of great significance. In this study, the interfacial structure of surface micropores filled with Sn-Ag-Cu or Sn-Ag-Cu/whisker carbon nanotubes (whiskerCNT) of M50 material was prepared by laser additive manufacturing and high-temperature infiltration. From 2 to 22 N, the lubrication characteristics of Sn-Ag-Cu and whiskerCNT in surface micropores of M50 was investigated.

Enhancing Sketch-Based Image Retrieval by CNN Semantic Re-ranking.

This paper introduces a convolutional neural network (CNN) semantic re-ranking system to enhance the performance of sketch-based image retrieval (SBIR). Distinguished from the existing approaches, the proposed system can leverage category information brought by CNNs to support effective similarity measurement between the images. To achieve effective classification of query sketches and high-quality initial retrieval results, one CNN model is trained for classification of sketches, another for that of natural images.

Establishment of peripheral blood mononuclear cell-derived humanized lung cancer mouse models for studying efficacy of PD-L1/PD-1 targeted immunotherapy.

Animal models used to evaluate efficacies of immune checkpoint inhibitors are insufficient or inaccurate. We thus examined two xenograft models used for this purpose, with the aim of optimizing them. One method involves the use of peripheral blood mononuclear cells and cell line-derived xenografts (PBMCs-CDX model).

Joint Hypergraph Learning for Tag-Based Image Retrieval.

As the image sharing websites like Flickr become more and more popular, extensive scholars concentrate on tag-based image retrieval. It is one of the important ways to find images contributed by social users. In this research field, tag information and diverse visual features have been investigated.

POI Summarization by Aesthetics Evaluation From Crowd Source Social Media.

Place-of-Interest (POI) summarization by aesthetics evaluation can recommend a set of POI images to the user and it is significant in image retrieval. In this paper, we propose a system that summarizes a collection of POI images regarding both aesthetics and diversity of the distribution of cameras. First, we generate visual albums by a coarse-to-fine POI clustering approach and then generate 3D models for each album by the collected images from social media.